Special Physical Characteristic and Training Strategy of Badminton Based on Machine Learning Algorithm

Abstract

Machine learning algorithms involve many fields such as statistics and probability theory. It is mainly aimed at computer simulation or execution of human learning behaviors to acquire new knowledge and skills. And the existing knowledge is to be integrated and then continue to be improved. This study aims to introduce a machine learning algorithm to establish a corresponding training effect evaluation model, to use the model to research and analyze the special physical fitness training strategy of badminton, and to discuss the appropriate badminton physical fitness training method. At the same time, the Markov model is used to compare the effect of the machine learning algorithm model. The experimental results show that the combination of four kinds of training of phosphate energy supply capacity, speed, sensitivity, and core strength can improve the training performance of badminton players by 10 points and the training efficiency by 4%. Therefore, it is necessary to comprehensively and reasonably arrange the four physical training methods to achieve better training effects. The average evaluation and prediction accuracies of the machine learning algorithm model were 86% and 87%, respectively, and the computing time was 26.7 ms and 13.6 ms. The average evaluation and prediction accuracies of the Markov model were 79% and 82%, respectively, and the computing time was 19.5 ms and 11.8 ms. It can be seen that the model based on the machine learning algorithm is more accurate than the Markov model.

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